3D airway geometry analysis of factors in airway navigation failure for lung nodules.

ABSTRACT:

Background

This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure.

Methods

We retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit ( http://www.vmtk.org ), simple insight toolkit ( https://sitk.org ), and sci-kit image ( https://scikit-image.org ). We used a machine learning-based approach to explore the utility of these significant factors.

Result

SUBMITTER: Cho HH 

PROVIDER: S-EPMC11223435 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature